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Version 0.4.1
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VPetukhov committed Oct 30, 2020
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3 changes: 1 addition & 2 deletions CHANGELOG.md
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## Upcoming
## [0.4.1] — 2020-10-30

### Added

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- `find_grid_point_labels_kde` now preserves label ids
- Fixed docker build
- Added ImageMagick dependency to fix problems with DAPI prior

2 changes: 1 addition & 1 deletion Project.toml
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name = "Baysor"
uuid = "cc9f9468-1fbe-11e9-0acf-e9460511877c"
authors = ["Viktor Petukhov <[email protected]>"]
version = "0.4.0"
version = "0.4.1"

[deps]
ArgParse = "c7e460c6-2fb9-53a9-8c5b-16f535851c63"
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9 changes: 9 additions & 0 deletions README.md
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**Bay**esian **S**egmentation **o**f Spatial T**r**anscriptomics Data

- [News ([0.4.1] — 2020-10-30)](#news-041--2020-10-30)
- [Abstract](#abstract)
- [Method description](#method-description)
- [Installation](#installation)
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- [Choice of parameters](#choice-of-parameters)
- [Multi-threading](#multi-threading)

## News ([0.4.1] — 2020-10-30)

- Saving NCV colors to the `ncv_color` field of *segmentation.csv*
- Dropped support for julia < 1.5
- Fixed docker build

*See the [changelog](CHANGELOG.md) for more detalis.*

## Abstract

Spatial transcriptomics is an emerging stack of technologies, which adds spatial dimension to conventional single-cell RNA-sequencing. New protocols, based on in situ sequencing or multiplexed RNA fluorescent in situ hybridization register positions of single molecules in fixed tissue slices. Analysis of such data at the level of individual cells, however, requires accurate identification of cell boundaries. While many existing methods are able to approximate cell center positions using nuclei stains, current protocols do not report robust signal on the cell membranes, making accurate cell segmentation a key barrier for downstream analysis and interpretation of the data. To address this challenge, we developed a tool for **Bay**esian **S**egmentation **o**f Spatial T**r**anscriptomics Data (Baysor), which optimizes segmentation considering the likelihood of transcriptional composition, size and shape of the cell. The Bayesian approach can take into account nuclear or cytoplasm staining, however can also perform segmentation based on the detected transcripts alone. We show that Baysor segmentation can in some cases nearly double the number of the identified cells, while reducing contamination. Importantly, we demonstrate that Baysor performs well on data acquired using five different spatially-resolved protocols, making it a useful general tool for analysis of high-resolution spatial data.
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